README.md

---
license: cc-by-nc-4.0
language:
- ar
task_categories:
- image-classification
- image-text-to-text
task_ids:
- multi-label-image-classification
pretty_name: AHA-Memes
size_categories:
- 10K<n<100K
tags:
- hate-speech-detection
- memes
- multimodal
- arabic
- content-moderation
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- found
source_datasets:
- original
extra_gated_prompt: >-
  This dataset contains hateful, offensive and disturbing material, including
  slurs and dehumanising imagery targeting protected groups. It is released for
  research on hate-speech detection and content moderation under CC BY-NC 4.0
  (non-commercial). By requesting access you confirm that you will use it for
  research purposes only and will not redistribute it or use it to target,
  profile or harm any individual or group.
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*.parquet
  - split: dev
    path: data/dev-*.parquet
  - split: test
    path: data/test-*.parquet
  - split: silver
    path: data/silver-*.parquet
dataset_info:
  features:
  - name: id
    dtype: string
  - name: image
    dtype: image
  - name: text
    dtype: string
  - name: label
    dtype: string
  - name: fine_grained_label
    sequence: string
  - name: annotation_source
    dtype: string
  - name: meta
    struct:
    - name: dialect
      dtype: string
    - name: visual_manipulation
      dtype: string
    - name: ocr_text
      dtype: string
    - name: ocr_english_translation
      dtype: string
    - name: stance
      dtype: string
    - name: sentiment
      dtype: string
    - name: text_image_relationship
      dtype: string
    - name: meaning_type
      dtype: string
    - name: context_scope
      dtype: string
    - name: english_rationale
      dtype: string
    - name: arabic_rationale
      dtype: string
    - name: confidence
      dtype: string
    - name: subtype_raw
      dtype: string
    - name: topic
      sequence: string
    - name: mentioned_categories
      sequence: string
    - name: intent
      sequence: string
    - name: emotion
      sequence: string
    - name: cultural_references
      sequence: string
    - name: propaganda_techniques
      sequence: string
    - name: requires_current_event_knowledge
      dtype: bool
    - name: propaganda
      dtype: bool
  splits:
  - name: train
    num_examples: 3500
  - name: dev
    num_examples: 500
  - name: test
    num_examples: 1000
  - name: silver
    num_examples: 66413
---

# AHA-Memes

**A Fine-Grained Multimodal Benchmark for Understanding Hate in Arabic Memes**

Hateful memes carry their meaning in the interaction between an image and the text
laid over it, and often through cultural references that neither modality states
outright. Arabic has been badly served here: the meme resources that exist
annotate propaganda or coarse "harmful content", not who is being attacked or how.

AHA-Memes is a benchmark of **5,000 Arabic memes**, each annotated by trained
native speakers for binary hatefulness and, where a meme is hateful, for the
*attack strategy* it uses. A further **66,413 memes** ship with labels and rich
descriptive metadata generated by Gemini 3.1 Pro, for weakly supervised and
semi-supervised work.

> ⚠️ **Content warning.** This dataset contains hateful, offensive and disturbing
> material, including slurs and dehumanising imagery targeting protected groups.
> It is released to enable research on detection and moderation, not to endorse
> any of it.

- **Paper:** [arXiv:2607.27393](https://arxiv.org/abs/2607.27393)
- **Code, baselines and reproduction recipes:** [github.com/MohamedBayan/AHA-Memes](https://github.com/MohamedBayan/AHA-Memes)
- **Licence:** CC BY-NC 4.0 (non-commercial research)

## Loading it

```python
from datasets import load_dataset

test = load_dataset("QCRI/AHA-MEMES", split="test")
print(test[0]["label"], test[0]["fine_grained_label"])

# The silver corpus is ~5 GB; stream it rather than downloading it whole.
silver = load_dataset("QCRI/AHA-MEMES", split="silver", streaming=True)
```

If you want files on disk instead — which is what the image and fusion baselines
in the code repository expect — use the downloader there:

```bash
python -m aha.download                  # train + dev + test
python -m aha.download --splits silver
```

## Splits

| Split | Memes | Hateful | Labels | Size |
|---|---:|---:|---|---:|
| `train` | 3,500 | 1,324 (37.8%) | human | 0.27 GB |
| `dev` | 500 | 189 (37.8%) | human | 0.04 GB |
| `test` | 1,000 | 337 (33.7%) | human | 0.08 GB |
| `silver` | 66,413 | 580 (0.9%) | Gemini 3.1 Pro, **not verified** | 4.95 GB |

The 5,000 human-annotated memes are the benchmark. They are stratified by the
binary label, no meme appears in more than one split, and `silver` is disjoint
from all three. All results in the paper come from the human splits.

## Fields

| Field | Type | Notes |
|---|---|---|
| `id` | string | original filename, e.g. `F9yEY9jXIAAJUTg.jpg` |
| `image` | image | the meme |
| `text` | string | text overlaid on the meme, extracted with EasyOCR |
| `label` | string | `Hateful` / `Not Hateful`; null for 116 silver rows |
| `fine_grained_label` | list[string] | subset of the ten categories below |
| `annotation_source` | string | `human`, or `gemini-3.1-pro-preview` on `silver` |
| `meta` | struct | 21 descriptive attributes, **all model-generated** |

**Check `annotation_source` before treating a label as ground truth.** It is the
only thing distinguishing the human benchmark from the silver corpus once the
splits are concatenated.

### The taxonomy

Binary hatefulness sits at the top. A meme is *Hateful* if it attacks people,
directly or indirectly, on the basis of a protected characteristic. Two
boundaries are deliberate: attacks on groups that themselves perpetrate hate are
not counted, and content that is merely rude or offensive without targeting a
protected category is *Not Hateful*.

The ten fine-grained categories are multi-label and conditional on that decision:

| Hateful — attack strategy | Not hateful — pragmatic function |
|---|---|
| Mocking, Incitement, Dehumanization, Slurs, Contempt, Inferiority, Exclusion | Humor, Sarcasm |
| `Other` | `Other` |

`Other` is reachable from either side, which is why the paper's split table lists
it twice. Full definitions and the bilingual guidelines the annotators worked from
are in [`docs/annotation-guidelines.md`](https://github.com/MohamedBayan/AHA-Memes/blob/main/docs/annotation-guidelines.md).

### Label distribution

| Hate | Fine-grained | Train | Dev | Test | Total |
|---|---|---:|---:|---:|---:|
| Hateful | Mocking | 706 | 90 | 211 | 1,007 |
| Hateful | Incitement | 320 | 51 | 85 | 456 |
| Hateful | Dehumanization | 247 | 42 | 58 | 347 |
| Hateful | Slurs | 252 | 42 | 47 | 341 |
| Hateful | Contempt | 107 | 18 | 50 | 175 |
| Hateful | Inferiority | 57 | 14 | 32 | 103 |
| Hateful | Exclusion | 10 | 4 | 3 | 17 |
| Hateful | Other | 18 | 2 | 7 | 27 |
| Not Hateful | Other | 380 | 50 | 96 | 526 |
| Not Hateful | Sarcasm | 934 | 126 | 333 | 1,393 |
| Not Hateful | Humor | 863 | 136 | 332 | 1,331 |
| | **Memes** | **3,500** | **500** | **1,000** | **5,000** |

Counts exceed the number of memes because the label is multi-label: 9.9% of train
and 21.4% of test carry more than one category. The taxonomy is long-tailed —
*Exclusion* has three test instances — which is why macro-F1 reads far below
micro-F1 for every system.

### The `meta` struct

Generated by Gemini 3.1 Pro for every row. On the human splits it was produced
*conditioned on the human label*, so the model described the meme but never
relabelled it; on `silver` the same call produced both label and description.

`topic`, `mentioned_categories`, `dialect`, `visual_manipulation`, `ocr_text`,
`ocr_english_translation`, `intent`, `stance`, `sentiment`, `emotion`,
`text_image_relationship`, `meaning_type`, `cultural_references`, `context_scope`,
`requires_current_event_knowledge`, `propaganda`, `propaganda_techniques`,
`english_rationale`, `arabic_rationale`, `confidence`, `subtype_raw`.

Two are easy to misread. `meta.ocr_text` is not the same as the top-level `text`:
the former is what Gemini read off the image, the latter is EasyOCR's output, and
they disagree often enough to be useful if you care about OCR quality. And
`mentioned_categories` is a model guess, not a human target annotation — the human
target labels are not part of this release.

## Known quirks in the silver split

- **116 rows have no label.** For 115 the model's reply could not be parsed; one
  parsed but omitted the label. They still carry image and OCR text, so they are
  usable as unlabelled data. Filter with `ds.filter(lambda x: x["label"] is not None)`.
- **Three rows got an out-of-taxonomy category** (`Criticism` ×2, `Satire` ×1).
  `fine_grained_label` is left empty for those so the label space stays closed;
  the literal reply is preserved in `meta.subtype_raw`.
- **Silver fine-grained labels are single-label.** The prompt asked for one
  subtype, so unlike the human splits these never have more than one element.
  Do not mix the two when computing multi-label statistics.
- **The class balance differs sharply.** 0.9% of silver is hateful against 37% of
  the human splits. The human set was built by pre-selecting candidates with
  Gemma-3-12B to raise the positive rate; silver covers the broad pool.
- **It skews Egyptian.** 56,697 of the 66,297 labelled silver rows are tagged
  `egyptian` dialect and `daily_life` is the dominant topic. Large, but not evenly
  spread across the Arabic-speaking world.

## How it was built

Memes were collected from public pages and groups on Facebook, Instagram,
Pinterest and Twitter/X, focused on public figures, politics and social
commentary. Exact and near-duplicate images were removed using embeddings from a
model fine-tuned on social-media imagery, treating pairs within Euclidean
distance 3.6 as duplicates. Overlaid text was extracted with
[EasyOCR](https://github.com/JaidedAI/EasyOCR); memes with no detectable text
were dropped, so every meme here has both modalities.

Because hateful content is rare in the wild, Gemma-3-12B assigned provisional
binary labels to 71K memes and the 5,000-meme annotation set was sampled from that
pool. Those provisional labels were hidden from annotators and discarded — all
gold labels are human.

Annotation was done by a third-party company: three trained native Arabic
speakers working from bilingual guidelines, after several rounds of training and
guideline refinement, at a cost of roughly $4K. Agreement (Cohen's κ,
macro-averaged over subtypes and annotator pairs) was 0.91 for binary
hatefulness, 0.75 for hate type and 0.67 for the non-hateful subtypes.

## Benchmark results

From the paper's Table 2, on the 1,000-meme test split. Regenerate any of these
from the committed predictions in the code repository — no GPU needed.

| System | Binary macro-F1 | Binary Rec(Hate) | Fine-grained macro-F1 |
|---|---:|---:|---:|
| Majority baseline | 0.399 | 0.000 | 0.050 |
| MARBERTv2 (text, fine-tuned) | 0.709 | 0.596 | 0.263 |
| MARBERTv2+BEiT (late fusion) | 0.724 | **0.656** | 0.318 |
| Qwen3-VL-8B-Instruct (zero-shot) | 0.643 | 0.318 | 0.176 |
| Qwen3-VL-8B (LoRA fine-tuned) | **0.768** | **0.656** | 0.334 |
| Gemini-2.5-pro (zero-shot) | 0.711 | 0.457 | **0.340** |
| GPT-5 (zero-shot) | 0.628 | 0.282 | 0.301 |

Fine-tuning the open 8B VLM wins the binary task. Zero-shot models are badly
under-sensitive — InternVL3.5-8B recalls 6% of hateful memes while posting 0.676
accuracy, barely above the majority baseline. And nothing clears 0.35 macro-F1 on
the fine-grained task, where the best system is a *prompted* closed model: 3,500
training memes are not enough for the long tail.

## Intended use and limitations

Intended for research on Arabic multimodal hate detection and safer content
moderation. Not for commercial use, and not for targeting, profiling or otherwise
harming individuals or groups.

The data comes from four public platforms and does not cover every dialect,
region or platform community, nor private or ephemeral content. Some annotation
boundaries are genuinely hard — offensive humour, satire, political criticism and
protected-group hate shade into one another, which is what the detailed guidelines
and the calibration rounds were for. The silver corpus broadens the resource but
its labels are model-generated and unverified. Models trained here can both miss
harmful content and over-flag legitimate speech, so they belong behind human
oversight rather than in front of it.

## Citation

```bibtex
@article{kmainasi2026aha,
  title={AHA-Memes: A Fine-Grained Multimodal Benchmark for Understanding Hate in Arabic Memes},
  author={Kmainasi, Mohamed Bayan and Shahroor, Ali Ezzat and Hasnat, Abul and Biswas, Md Rafiul and Zaghouani, Wajdi and Alam, Firoj},
  journal={arXiv preprint arXiv:2607.27393},
  year={2026}
}
```

## Acknowledgments

Supported by NPRP grant 14C-0916-210015 from the Qatar National Research Fund,
part of the Qatar Research Development and Innovation Council (QRDI). The
findings reported here are solely the responsibility of the authors.